The analysis of breathing and rhythm in speech

Speech rhythm can be described as the temporal patterning by which speech events, such as vocalic onsets, occur. Despite efforts to quantify and model speech rhythm across languages, it remains a scientifically enigmatic aspect of prosody. For instance, one challenge lies in determining how to best quantify and analyse speech rhythm. Techniques range from manual phonetic annotation to the automatic extraction of acoustic features. It is currently unclear how closely these differing approaches correspond to one another. Moreover, the primary means of speech rhythm research has been the analysis of the acoustic signal only. Investigations of speech rhythm may instead benefit from a range of complementary measures, including physiological recordings, such as of respiratory effort. This thesis therefore combines acoustic recording with inductive plethysmography (breath belts) to capture temporal characteristics of speech and speech breathing rhythms. The first part examines the performance of existing phonetic and algorithmic techniques for acoustic prosodic analysis in a new corpus of rhythmically diverse English and Mandarin speech. The second part addresses the need for an automatic speech breathing annotation technique by developing a novel function that is robust to the noisy plethysmography typical of spontaneous, naturalistic speech production. These methods are then applied in the following section to the analysis of English speech and speech breathing in a second, larger corpus. Finally, behavioural experiments were conducted to investigate listeners' perception of speech breathing using a novel gap detection task. The thesis establishes the feasibility, as well as limits, of automatic methods in comparison to manual annotation. In the speech breathing corpus analysis, they help show that speakers maintain a normative, yet contextually adaptive breathing style during speech. The perception experiments in turn demonstrate that listeners are sensitive to the violation of these speech breathing norms, even if unconsciously so. The thesis concludes by underscoring breathing as a necessary, yet often overlooked, component in speech rhythm planning and production

A Study of Accomodation of Prosodic and Temporal Features in Spoken Dialogues in View of Speech Technology Applications

Inter-speaker accommodation is a well-known property of human speech and human interaction in general. Broadly it refers to the behavioural patterns of two (or more) interactants and the effect of the (verbal and non-verbal) behaviour of each to that of the other(s). Implementation of thisbehavior in spoken dialogue systems is desirable as an improvement on the naturalness of humanmachine interaction. However, traditional qualitative descriptions of accommodation phenomena do not provide sufficient information for such an implementation. Therefore, a quantitativedescription of inter-speaker accommodation is required. This thesis proposes a methodology of monitoring accommodation during a human or humancomputer dialogue, which utilizes a moving average filter over sequential frames for each speaker. These frames are time-aligned across the speakers, hence the name Time Aligned Moving Average (TAMA). Analysis of spontaneous human dialogue recordings by means of the TAMA methodology reveals ubiquitous accommodation of prosodic features (pitch, intensity and speech rate) across interlocutors, and allows for statistical (time series) modeling of the behaviour, in a way which is meaningful for implementation in spoken dialogue system (SDS) environments.In addition, a novel dialogue representation is proposed that provides an additional point of view to that of TAMA in monitoring accommodation of temporal features (inter-speaker pause length and overlap frequency). This representation is a percentage turn distribution of individual speakercontributions in a dialogue frame which circumvents strict attribution of speaker-turns, by considering both interlocutors as synchronously active. Both TAMA and turn distribution metrics indicate that correlation of average pause length and overlap frequency between speakers can be attributed to accommodation (a debated issue), and point to possible improvements in SDS “turntaking” behaviour. Although the findings of the prosodic and temporal analyses can directly inform SDS implementations, further work is required in order to describe inter-speaker accommodation sufficiently, as well as to develop an adequate testing platform for evaluating the magnitude ofperceived improvement in human-machine interaction. Therefore, this thesis constitutes a first step towards a convincingly useful implementation of accommodation in spoken dialogue systems

AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response

Scammers are aggressively leveraging AI voice-cloning technology for social engineering attacks, a situation significantly worsened by the advent of audio Real-time Deepfakes (RTDFs). RTDFs can clone a target's voice in real-time over phone calls, making these interactions highly interactive and thus far more convincing. Our research confidently addresses the gap in the existing literature on deepfake detection, which has largely been ineffective against RTDF threats. We introduce a robust challenge-response-based method to detect deepfake audio calls, pioneering a comprehensive taxonomy of audio challenges. Our evaluation pitches 20 prospective challenges against a leading voice-cloning system. We have compiled a novel open-source challenge dataset with contributions from 100 smartphone and desktop users, yielding 18,600 original and 1.6 million deepfake samples. Through rigorous machine and human evaluations of this dataset, we achieved a deepfake detection rate of 86% and an 80% AUC score, respectively. Notably, utilizing a set of 11 challenges significantly enhances detection capabilities. Our findings reveal that combining human intuition with machine precision offers complementary advantages. Consequently, we have developed an innovative human-AI collaborative system that melds human discernment with algorithmic accuracy, boosting final joint accuracy to 82.9%. This system highlights the significant advantage of AI-assisted pre-screening in call verification processes. Samples can be heard at https://mittalgovind.github.io/autch-samples/Comment: Dataset will be made public by end of March 202

Evaluating pause particles and their functions in natural and synthesized speech in laboratory and lecture settings

Pause-internal phonetic particles (PINTs) comprise a variety of phenomena including: phonetic-acoustic silence, inhalation and exhalation breath noises, filler particles “uh” and “um” in English, tongue clicks, and many others. These particles are omni-present in spontaneous speech, however, they are under-researched in both natural speech and synthetic speech. The present work explores the influence of PINTs in small-context recall experiments, develops a bespoke speech synthesis system that incorporates the PINTs pattern of a single speaker, and evaluates the influence of PINTs on recall for larger material lengths, namely university lectures. The benefit of PINTs on recall has been documented in natural speech in small-context laboratory settings, however, this area of research has been under-explored for synthetic speech. We devised two experiments to evaluate if PINTs have the same recall benefit for synthetic material that is found with natural material. In the first experiment, we evaluated the recollection of consecutive missing digits for a randomized 7-digit number. Results indicated that an inserted silence improved recall accuracy for digits immediately following. In the second experiment, we evaluated sentence recollection. Results indicated that sentences preceded by an inhalation breath noise were better recalled than those with no inhalation. Together, these results reveal that in single-sentence laboratory settings PINTs can improve recall for synthesized speech. The speech synthesis systems used in the small-context recall experiments did not provide much freedom in terms of controlling PINT type or location. Therefore, we endeavoured to develop bespoke speech synthesis systems. Two neural text-to-speech (TTS) systems were created: one that used PINTs annotation labels in the training data, and another that did not include any PINTs labeling in the training material. The first system allowed fine-tuned control for inserting PINTs material into the rendered material. The second system produced PINTs probabilistally. To the best of our knowledge, these are the first TTS systems to render tongue clicks. Equipped with greater control of synthesized PINTs, we returned to evaluating the recall benefit of PINTs. This time we evaluated the influence of PINTs on the recollection of key information in lectures, an ecologically valid task that focused on larger material lengths. Results indicated that key information that followed PINTs material was less likely to be recalled. We were unable to replicate the benefits of PINTs found in the small-context laboratory settings. This body of work showcases that PINTs improve recall for TTS in small-context environments just like previous work had indicated for natural speech. Additionally, we’ve provided a technological contribution via a neural TTS system that exerts finer control over PINT type and placement. Lastly, we’ve shown the importance of using material rendered by speech synthesis systems in perceptual studies.This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the project “Pause-internal phonetic particles in speech communication” (project number: 418659027; project IDs: MO 597/10-1 and TR 468/3-1). Associate member of SFB1102 “Information Density and Linguistic Encoding” (project number: 232722074)

Listeners are sensitive to the speech breathing time series: Evidence from a gap detection task

The effect of non-speech sounds, such as breathing noise, on the perception of speech timing is currently unclear. In this paper we report the results of three studies investigating participants' ability to detect a silent gap located adjacent to breath sounds during naturalistic speech. Experiment 1 (n = 24, in-person) asked whether participants could either detect or locate a silent gap that was added adjacent to breath sounds during speech. In Experiment 2 (n = 182; online), we investigated whether different placements within an utterance were more likely to elicit successful detection of gaps. In Experiment 3 (n = 102; online), we manipulated the breath sounds themselves to examine the effect of breath-specific characteristics on gap identification. Across the study, we document consistent effects of gap duration, as well as gap placement. Moreover, in Experiment 2, whether a gap was positioned before or after an interjected breath significantly predicted accuracy as well as the duration threshold at which gaps were detected, suggesting that nonverbal aspects of audible speech production specifically shape listeners' temporal expectations. We also describe the influences of the breath sounds themselves, as well as the surrounding speech context, that can disrupt objective gap detection performance. We conclude by contextualising our findings within the literature, arguing that the verbal acoustic signal is not "speech itself" per se, but rather one part of an integrated percept that includes speech-related respiration, which could be more fully explored in speech perception studies

The dawn of the human-machine era: a forecast of new and emerging language technologies

New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world's smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawn

The SSPNet-Mobile Corpus: from the detection of non-verbal cues to the inference of social behaviour during mobile phone conversations

Mobile phones are one of the main channels of communication in contemporary society. However, the effect of the mobile phone on both the process of and, also, the non-verbal behaviours used during conversations mediated by this technology, remain poorly understood. This thesis aims to investigate the role of the phone on the negotiation process as well as, the automatic analysis of non-verbal behavioural cues during conversations using mobile telephones, by following the Social Signal Processing approach. The work in this thesis includes the collection of a corpus of 60 mobile phone conversations involving 120 subjects, development of methods for the detection of non-verbal behavioural events (laughter, fillers, speech and silence) and the inference of characteristics influencing social interactions (personality traits and conflict handling style) from speech and movements while using the mobile telephone, as well as the analysis of several factors that influence the outcome of decision-making processes while using mobile phones (gender, age, personality, conflict handling style and caller versus receiver role). The findings show that it is possible to recognise behavioural events at levels well above chance level, by employing statistical language models, and that personality traits and conflict handling styles can be partially recognised. Among the factors analysed, participant role (caller versus receiver) was the most important in determining the outcome of negotiation processes in the case of disagreement between parties. Finally, the corpus collected for the experiments (the SSPNet-Mobile Corpus) has been used in an international benchmarking campaign and constitutes a valuable resource for future research in Social Signal Processing and more generally in the area of human-human communication

The phonetics of speech breathing : pauses, physiology, acoustics, and perception

Speech is made up of a continuous stream of speech sounds that is interrupted by pauses and breathing. As phoneticians are primarily interested in describing the segments of the speech stream, pauses and breathing are often neglected in phonetic studies, even though they are vital for speech. The present work adds to a more detailed view of both pausing and speech breathing with a special focus on the latter and the resulting breath noises, investigating their acoustic, physiological, and perceptual aspects. We present an overview of how a selection of corpora annotate pauses and pause-internal particles, as well as a recording setup that can be used for further studies on speech breathing. For pauses, this work emphasized their optionality and variability under different tempos, as well as the temporal composition of silence and breath noise in breath pauses. For breath noises, we first focused on acoustic and physiological characteristics: We explored alignment between the onsets and offsets of audible breath noises with the start and end of expansion of both rib cage and abdomen. Further, we found similarities between speech breath noises and aspiration phases of /k/, as well as that breath noises may be produced with a more open and slightly more front place of articulation than realizations of schwa. We found positive correlations between acoustic and physiological parameters, suggesting that when speakers inhale faster, the resulting breath noises were more intense and produced more anterior in the mouth. Inspecting the entire spectrum of speech breath noises, we showed relatively flat spectra and several weak peaks. These peaks largely overlapped with resonances reported for inhalations produced with a central vocal tract configuration. We used 3D-printed vocal tract models representing four vowels and four fricatives to simulate in- and exhalations by reversing airflow direction. We found the direction to not have a general effect for all models, but only for those with high-tongue configurations, as opposed to those that were more open. Then, we compared inhalations produced with the schwa-model to human inhalations in an attempt to approach the vocal tract configuration in speech breathing. There were some similarities, however, several complexities of human speech breathing not captured in the models complicated comparisons. In two perception studies, we investigated how much information listeners could auditorily extract from breath noises. First, we tested categorizing different breath noises into six different types, based on airflow direction and airway usage, e.g. oral inhalation. Around two thirds of all answers were correct. Second, we investigated how well breath noises could be used to discriminate between speakers and to extract coarse information on speaker characteristics, such as age (old/young) and sex (female/male). We found that listeners were able to distinguish between two breath noises coming from the same or different speakers in around two thirds of all cases. Hearing one breath noise, classification of sex was successful in around 64%, while for age it was 50%, suggesting that sex was more perceivable than age in breath noises.Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 418659027: "Pause-internal phonetic particles in speech communication